AbstractMotivation: Comparing protein structures is important
for understanding the relationships between sequence,
structure and function. With the increasing number of
experimentally determined protein structures, an automated
algorithm that can perform structure alignment of many
proteins is therefore highly advantageous.Results: We present a novel approach for multiple
structure alignment of proteins based on fuzzy pairwise
alignments of each protein to a virtual consensus chain.
These alignments are alternated with translations and
rotations of the proteins onto the consensus structure, and
with updating each consensus atom by moving it to the middle
of the protein atoms aligned to it. The pairwise alignments
use mean-field annealing optimization of fuzzy alignment
variables, based on a cost expressed in terms of distances
between aligned atoms and of gaps. No initialization in terms
of all-to-all protein alignments is needed, and the only
information required is the 3-D coordinates of the $C_\alpha$
atoms. The CPU consumption is modest, and scales
approximately linearly with the number of proteins to align.
Our approach is tested against a set of protein families from
the \textsc{Homstrad} database, and against a multiple
structure alignment algorithm based on Monte Carlo
techniques, with good results.